casedrop_reliability: Edge-weight Case-dropping Stability

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Edge-weight Case-dropping Stability

Description

Computes a CS-coefficient for the edge-weight vector of a network: the maximum proportion of cases (rows of x$data) that can be dropped while the flattened edge-weight vector of the re-estimated network still correlates with the original above threshold in at least certainty of iterations.

Plots the four model-level reliability metrics across drop proportions: correlation, mean_abs_dev, median_abs_dev, max_abs_dev. Each panel shows the per-iteration mean with a ribbon at mean +/- sd. The correlation panel includes a dashed horizontal line at the user's threshold (default 0.7).

Overlay of per-cluster correlation curves across drop proportions. One colour per sub-network; ribbons show mean +/- sd across iterations. Dashed horizontal line marks the stability threshold (default 0.7).

Usage

casedrop_reliability(
  x,
  iter = 1000L,
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  method = c("spearman", "pearson", "kendall"),
  include_diag = FALSE,
  seed = NULL
)

## S3 method for class 'net_casedrop_reliability'
print(x, digits = 3, ...)

## S3 method for class 'net_casedrop_reliability'
summary(object, ...)

## S3 method for class 'net_casedrop_reliability_group'
print(x, ...)

## S3 method for class 'net_casedrop_reliability_group'
summary(object, drop_prop = NULL, ...)

## S3 method for class 'summary.net_casedrop_reliability_group'
print(x, ...)

## S3 method for class 'net_casedrop_reliability'
plot(x, combined = TRUE, ...)

## S3 method for class 'net_casedrop_reliability_group'
plot(
  x,
  metric = c("correlation", "mean_abs_dev", "median_abs_dev", "max_abs_dev"),
  ...
)

Arguments

x	A net_casedrop_reliability_group object.
iter	Integer. Iterations per drop proportion. Default 1000.
drop_prop	Drop proportion at which to report the four metrics (mean +/- sd per network). Must be one of the drop proportions the object was built with. Defaults to the object's median grid value (the stored grid is used, not an assumed 0.7); pass an explicit value not in the grid to get an error listing the available proportions.
threshold	Numeric in ⁠[0, 1]⁠. Minimum edge-vector correlation for an iteration to count as stable. Default 0.7.
certainty	Numeric in ⁠[0, 1]⁠. Required fraction of iterations whose correlation must exceed threshold for a drop proportion to qualify. Default 0.95.
method	Correlation method: "pearson" (weight magnitudes), "spearman" (ranks, robust to scale), or "kendall". Default "spearman" because edge weights often span several orders of magnitude and rank stability is the typical target.
include_diag	Logical. Include diagonal (self-loop) edges in the edge vector. Default FALSE.
seed	Optional integer for reproducibility.
digits	Digits to display. Default 3.
...	Additional arguments (ignored).
object	A net_casedrop_reliability_group.
combined	When TRUE (default), all four metrics are shown in one ggplot via facet_wrap(~ metric). When FALSE, returns a named list of four single-panel ggplots, one per metric.
metric	Which metric to plot. One of "correlation" (default), "mean_abs_dev", "median_abs_dev", "max_abs_dev".

Details

Complements centrality_stability(): that function asks whether centrality rankings are stable; this one asks whether the edge-weight structure itself is stable. For MCML-derived networks where each row of ⁠$data⁠ is one transition, this is case-dropping of edges.

For each drop_prop p and each iteration, a size n_cases * (1 - p) subset of ⁠$data⁠ rows is selected without replacement, the network is re-estimated using the same method/scaling/threshold as the input, and the upper/lower-triangle (directed: all off-diagonal entries) of the new weight matrix is flattened and correlated with the corresponding vector of the original matrix. The correlation method defaults to Spearman for robustness to the wide dynamic range of transition probabilities.

Unlike bootstrap CIs, case-dropping does not estimate sampling variance and so does not rely on the i.i.d. assumption. This makes it the appropriate robustness check for edgelist-derived networks (where rows of ⁠$data⁠ lack actor grouping), since dropping rows at random is a well-posed operation regardless of within-actor correlation.

Value

An object of class net_casedrop_reliability with:

cs

Scalar CS-coefficient - the maximum drop proportion for which the edge-vector correlation remains >= threshold in at least certainty of iterations. Zero if no proportion qualifies.

correlations

iter x length(drop_prop) matrix of per- iteration correlations.

drop_prop, threshold, certainty, iter, method

Inputs.

The input x invisibly.

A tidy data frame with columns metric, drop_prop, mean, sd summarising edge-weight stability across case-dropping iterations.

A data frame with one row per network containing cor, mean_abs_dev, median_abs_dev, max_abs_dev formatted as "mean +/- sd".

A ggplot object, or a named list of four ggplots when combined = FALSE.

A ggplot object.

References

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods 50(1), 195-212. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-017-0862-1")}

See Also

centrality_stability(), bootstrap_network().

Examples

seqs <- data.frame(
  V1 = sample(LETTERS[1:4], 30, TRUE),
  V2 = sample(LETTERS[1:4], 30, TRUE),
  V3 = sample(LETTERS[1:4], 30, TRUE)
)
net <- build_network(seqs, method = "relative")
es  <- casedrop_reliability(net, iter = 50, drop_prop = c(0.1, 0.3, 0.5),
                      seed = 1)
print(es)

Nestimate documentation built on July 11, 2026, 1:09 a.m.